198 citations · 339 across the 18 of their papers we have counts for
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cs.NE2022
Optimisation & Generalisation in Networks of Neurons
Jeremy Bernstein
The goal of this thesis is to develop the optimisation and generalisation theoretic foundations of learning in artificial neural networks. On optimisation, a new theoretical framew…
cs.NE2021★ 5 cited
Learning by Turning: Neural Architecture Aware Optimisation
Yang Liu, Jeremy Bernstein, Markus Meister +1
Descent methods for deep networks are notoriously capricious: they require careful tuning of step size, momentum and weight decay, and which method will work best on a new benchmar…
cs.NE2020★ 5 cited
Learning compositional functions via multiplicative weight updates
Jeremy Bernstein, Jiawei Zhao, Markus Meister +3
Compositionality is a basic structural feature of both biological and artificial neural networks. Learning compositional functions via gradient descent incurs well known problems l…